{\huge Seeing Atoms}

How can we possibly see atoms? \vspace{.5in}

John Sidles is a medical researcher, a quantum systems engineer, whose major focus is on quantum spin microscopy for regenerative medicine. He is both Professor of Orthopedics and Sports Medicine in the University of Washington School of Medicine, and co-director of the UW Quantum Systems Engineering Lab. Watching various injury troubles at the Sochi Winter Olympics makes us wonder whether quantum sports medicine is an idea whose time has come. Well beyond overheated references to our athletes as ``warriors'' is a nice reality: John's main project is for healing those injured in the armed services.

Today Ken and I wish to talk about John and his work in general. We especially like his title of quantum systems engineer.

John works on the interplay between two important disciplines: medicine and quantum mechanics. One might be surprised that what we conceive as research in quantum mechanics is so closely related to medicine, but it is. John received the prestigious 2011 Günther Laukien Prize jointly with IBM researchers John Mamin and Dan Rugar. This was for their work on quantum spin microscopy, specifically, that variety of quantum spin microscopy called magnetic resonance force microscopy (MRFM).

\includegraphics[width=2in]{prize.png}

John is obviously very busy, yet he finds time to post thoughtful comments on GLL and elsewhere. His comments are always interesting, often more than anything that Ken and I have to say. We thank John for all his many contributions.

Catching the Waves

John works in a different world than we do, yet we feel that we have much in common. One difference, however, is that he is sometimes an author on a paper with a bit more co-authors that we routinely have: For example, this is the list of authors from a paper on gravity waves which appeared in Physical Review D\/:

\includegraphics[width=2in]{allJS.png}

Of course a Polymath paper can have even more co-authors, so perhaps we are not from such different worlds. In a sense this research is already Polymathic: it's part of a huge scientific collaboration to use a big machine named LIGO, which stands for Laser Interferometer Gravitational Wave Observatory.

There are actually two main observatories, in Livingston, Louisiana and Hanford near Richland, Washington. These are far enough apart to detect differences in arrival time of searched-for waves for purpose of confirmation. Amazingly the local observation of a wave would consist of a displacement of only $latex {10^{-18}}&fg=000000$ of a meter, a billionth of a nanometer, nano-nano. Based on mainstream cosmological models of the strength of gravity waves from stellar collision events, the current apparatus still projects only to have a $latex {1/6}&fg=000000$ chance of an unambiguous observation within a 6-year timespan. Here is a picture of LIGO's Hanford site, near where part of the Manhattan Project took place in 1943:

\includegraphics[width=2in]{LIGOHanford.jpg}

With $latex {10^{-18}}&fg=000000$ tolerance, it's a wonder that you couldn't be thrown off by a passing flock of birds. Well John's wife Connie, a noted Washington birder, could be posted as a lookout.

The LIGO paper reported ``nothing''---which in physics acts like a barrier or lower-bound theorem in computational theory. Technically it gave an upper bound---on the possible strength of gravity waves emanating from relatively-nearby X-ray binary stellar systems. What's neat is that gaining the 90% confidence in their result required not better observations of the very large, but rather control of the very small. Moreover LIGO itself is being updated with more-sensitive detectors, which should give better determination between models and measurements of the large. The fact that one can probe differences at all on finer than a $latex {10^{-18}m}&fg=000000$ scale is amazing, and it matters to all of John's work.

A Math Puzzle?

John works on ``seeing'' individual atoms. The ability to do this will have many revolutionary applications, including changing the way that medicine is performed. Given John's interest in regenerative medicine---the repair of damaged tissues and organs---the ability to see an individual atom, to see proteins in action, will have a potential game changing effect.

Okay seeing atoms is cool. But how is it possible? Here is a picture of a hydrogren atom:

\includegraphics[width=2in]{atom.png}

A question that Ken Steiglitz of Princeton raised a number of times to me, while I was on the faculty there, is this: Suppose you can only make objects with accuracy $latex {\delta}&fg=000000$, how can these be put together to make an object to tolerance $latex {\epsilon \ll \delta}&fg=000000$? This is the ``dual'' of seeing. Steiglitz's question for detection would be: Suppose you can only make objects with accuracy $latex {\delta}&fg=000000$, how can these be put together to detect an object of size $latex {\epsilon \ll \delta}&fg=000000$? Indeed. How is this possible?

A plausible conjecture is this: It is impossible to make objects with accuracy $latex {\epsilon}&fg=000000$, given only tools that have accuracy $latex {\delta}&fg=000000$ where $latex {\epsilon \ll \delta}&fg=000000$ The trouble is that this seems to be false. Everyday we---okay Intel and other companies---make chips that have object resolution below the $latex {30}&fg=000000$-nanometer range. Is this a counterexample? Or is it?

Let's look first at what John and his team have been able to do. Then let's return to the math problem raised here and see if the conjecture is actually true, false, or unresolved.

MRFM

One of John paper's on this work is here. It clearly is a computer theory paper since he uses Alice and Bob to explain what he is doing.

\includegraphics[width=2in]{pnas.png}

Magnetic resonance force microscopy (MRFM) is an imaging technique that I can define more easily by what it can do, than how it works. It can potentially see protein structures at scales beyond the depth of X-ray crystallography and protein nuclear magnetic resonance spectroscopy. It can detect image features that are not just beyond those of sports teams' magnetic resonance imaging (MRI) machines, but are over a billion times more detailed than those currently used in hospitals.

Recall atoms are small, but ``small'' means they are about $latex {10^{-10}}&fg=000000$ meters in size, on the order of $latex {10}&fg=000000$ nanometers. They are much too small to be seen with light-based microscopes. They can be ``seen'' by a MRFM. These combine various techniques. They use an MRI, which is based on quantum spin effects, and combine that with a probe that was used for an atomic force microscope.

Spacing Out Inner Space

Here is an example of how one can multiply the resolution of an object. The method is used in the lithography of integrated chips, to effectively double the resolution. We quote our friends at Wikipedia who use the following figure to explain this method:

\includegraphics[width=2in]{spacer.png}

A spacer is a film layer formed on the sidewall of a pre-patterned feature. A spacer is formed by deposition or reaction of the film on the previous pattern, followed by etching to remove all the film material on the horizontal surfaces, leaving only the material on the sidewalls. By removing the original patterned feature, only the spacer is left. However, since there are two spacers for every line, the line density has now doubled. The spacer technique is applicable for defining narrow gates at half the original lithographic pitch, for example. The spacer approach is unique in that with one lithographic exposure, the pitch can be halved indefinitely with a succession of spacer formation and pattern transfer processes. This conveniently avoids the serious issue of overlay between successive exposures.

Now let us return to our math puzzle. Here is an example from the Microscopy UK website. The grit is very fine so the epsilon is very small. Of course there still is the macro issue of the grinding motion and the amount of time spent doing that. But this seems to be okay with the conjecture: cannot one make epsilon smaller than the delta?

Open Problems

How low will we be able to go? Are computers really taking us beyond Einstein's tradeoff between scale and energy?